With the increasing availability of multichannel extracellular microelectrode arrays and the potentially large number of simultaneous recordings in neural system studies, data processing capability and capacity are very important. An automated on-line neural spike separation, sorted data collection and analysis system must strike a balance between performance, speed, and human intervention. The long-term objective of this work is to come up with a commercial automated system for identification of neural network topology. The objective of this proposal is to develop an automated multi-channel hardware subsystem for discrimination of neural spikes on-line. In this subsystem, an algorithm using the Haar transform basis. This algorithm will be totally automated from learning spike waveforms to classifying spikes and extended to multi-channel multiunit discrimination in hardware. The system will be tested in in vivo experiments. The development of a multi-channel discrimination and analysis system has the potential to make a great impact on traditional neurophysiology, biological and computational neural network research and neural control of prosthetic devices.